共同基金绩效的持续性

共同基金绩效的持续性Using a sample free of survivor bias, I demonstrate that common factors in stock returns and investment expenses almost completely explain persistence in equity mutual funds mean and risk-adjusted returns. Hendricks, Patel and Zeckhausers (1993) hot hands result is mostly driven by the one-year momentum effect of Jegadeesh and Titman (1993), but individual funds do not earn higher returns from following the momentum strategy in stocks. The only significant persistence not explained is concentrated in strong underperformance by the worst-return mutual funds. The results do not support the existence of skilled or informed mutual fund portfolio managers.使用免于幸存者偏见的样本，我表明股票回报及投资开支的共同因素，几乎完全解释了在股票型共同基金的平均值和风险调整后的回报有持久性。Patel和Zeckhauster（1993） “热手”的结果大多是由Jegadeesh和Titman（1993年）一年期的动量效应影响驱动的，但个别基金的动量策略并不通过跟进股票的动量策略来赚取更高的回报。唯一显著的没有解释的持久性主要集中在由最坏返回互惠基金造成的强烈的表现不佳。这些结果不支持技术或知情的互惠基金投资组合经理存在。PERSISTENCE IN MUTUAL FUND performance does not reflect superior stock-picking skill. Rather, common factors in stock returns and persistent differences in mutual fund expenses and transaction costs explain almost all of the predict- ability in mutual fund returns. Only the strong, persistent underperformance by the worst-return mutual funds remains anomalous. Mutual fund persistence is well documented in the finance literature, but not well explained. Hendricks, Patel, and Zeckhauser (1993), Goetzmann and Ibbotson (1994), Brown and Goetzmann (1995), and Wermers (1996) find evidence of persistence in mutual fund performance over short-term horizons of one to three years, and attribute the persistence to hot hands or common investment strategies. Grinblatt and Titman (1992), Elton, Gruber, Das, and Hlavka (1993), and Elton, Gruber, Das, and Blake (1996) document mutual fund return predictability over longer horizons of five to ten years, and at- tribute this to manager differential information or stock-picking talent. Con- trary evidence comes from Jensen (1969), who does not find that good subse- quent performance follows good past performance. Carhart (1992) shows that persistence in expense ratios drives much of the long-term persistence in mutual fund performance. My analysis indicates that Jegadeesh and Titmans (1993) one-year momen- tum in stock returns accounts for Hendricks, Patel, and Zeckhausers (1993) hot hands effect in mutual fund performance共同基金的持久性表现并不反映更卓越的选股技能。相反，普通股票回报的共同因素和共同基金的费用和交易成本的持续性差异，解释了几乎所有的共同基金回报的可预测性。只有由最坏返回互惠基金造成的强烈的持续的表现不佳依然反常。共同基金持续性在金融文献中有据可查但没有得到很好的解释。Hendricks, Patel, 和 Zeckhauser（1993） ，Goetzmann 和Ibbotson (1994), Brown 和 Goetzmann (1995)， Wermers （1996）发现在一至三年的短期视野中共同基金业绩有持久性的证据，并且将持久性归因于 “热手hothand效应”或共同的投资策略。矫正Grinblatt和Titman （1992） ，埃尔顿格鲁伯，达斯， Hlavka （1993） ，和埃尔顿，格鲁伯，达斯，和Blake （1996）证明共同基金的回报在更长的视野，五到十年有可预见性。归因于经理的差分信息或选股的天赋。相反的证据来自Jensen（1969），他没有找到，良好的后续表现（补救措施）并未跟随着良好的过往表现。Carhart（1992）展示，费用率的持久性驱动很多共同基金绩效的长期持久性。我的分析表明， Jegadeesh和Timan（1993）的股票回报一年期动量解释了Hendricks, Patel, and Zeckhause（1993）的共同基金绩效的热手效应。However, funds that earn higher one-year returns do so not because fund managers successfully follow momen- tum strategies, but because some mutual funds just happen by chance to hold relatively larger positions in last years winning stocks. Hot-hands funds infrequently repeat their abnormal performance. This is in contrast to Werm- ers (1996), who suggests that it is the momentum strategies themselves that generate short-term persistence, and Grinblatt, Titman, and Wermers (1995), who find that funds following momentum strategies realize better performance before management fees and transaction expenses. While measuring whether funds follow the momentum strategy is imperfect in my sample, individual mutual funds that appear to follow the one-year momentum strategy earn significantly lower abnormal returns after expenses. Thus, I conclude that transaction costs consume the gains from following a momentum strategy in stocks.然而，获得较高的一年期回报的基金这样不是因为基金经理成功地遵循动量策略，而是因为一些共同基金只是偶然在去年的优胜股票持有较大的头寸。短期持续性基金很少重复他们的异常表现。这是和Werm - ERS （1996）形成对比，Werm-ers表明这是的动量策略本身产生短期的持久性，Grinblatt ，Titman，和Wermers （1995） ，发现基金跟随动量策略在管理费用和交易费用产生之前实现更好的绩效表现。虽然衡量基金是否遵循动量策略在我的示例里不完善，但是个别表现出按照一年期的动量策略的共同基金在费用发生之后赚取异常报酬显着降低。因此，我的结论是交易成本会消耗实施动量投资策略的股票的收益。I demonstrate that expenses have at least a one-for-one negative impact on fund performance, and that turnover also negatively impacts performance. By my estimates, trading reduces performance by approximately 0.95 percent of the trades market value. Variation in costs per transaction across mutual funds also explains part of the persistence in performance. In addition, I find that fund performance and load fees are strongly and negatively related, probably due to higher total transaction costs for load funds. Holding expense ratios constant, load funds underperform no-load funds by approximately 80 basis points per year. (This figure ignores the load fees themselves.) 我证明，费用对基金业绩产生负面影响，而营业额也对绩效产生负面影响。根据我的估计，交易降低绩效贸易市场价值约0.95 。整个共同基金每笔交易成本的变化也解释了绩效持久性的一部分。另外，基金绩效和负载费用有很强的负相关，这可能是由于较高的总负载资金的交易成本。费用率保持不变，负载基金比起无负载的资金表现不佳每年约80个（有偏的）基点（这个数字忽略负载费用自身）。The joint-hypothesis problem of testing market efficiency conditional on the imposed equilibrium model of returns clouds what little evidence there is in this article to support the existence of mutual fund manager stock-picking skill. Funds with high past alphas demonstrate relatively higher alphas and expected returns in subsequent periods. However, these results are sensitive to model misspecification, since the same model is used to rank funds in both periods. In addition, these funds earn expected future alphas that are insig- nificantly different from zero. Thus, the best past-performance funds appear to earn back their expenses and transaction costs even though the majority underperform by approximately their investment costs. 、测试市场效率有条件的返回云层施加的均衡模型的联合假说问题，在这篇文章中，几乎没有什么证据支持共同基金经理选股技巧存在。与过去的高阿尔法基金表现出相对较高的阿尔法及在后续期间的预期回报。然而，这些结果是对模型的错误规格是敏感的，因为同样的模型被用于排列在这两个期间资金。此外，这些资金赚取预期未来阿尔法INSIG着异于零。因此，过去的表现最好的基金似乎他们的费用和交易费用赚回来，即使多数跑输大市约他们的投资成本。This study expands the existing literature by controlling for survivor bias, and by documenting common-factor and cost-based explanations for mutual fund persistence. Section I discusses the database and its relation to other survivor-bias corrected data sets. Section II presents models of performance measurement and their resulting pricing error estimates on passively-man- aged benchmark equity portfolios. Section III documents and explains the one-year persistence in mutual fund returns, and Section IV further interprets the results. Section V examines and explains longer-term persistence, and Section VI concludes.这项研究扩展现有的文献控制幸存者偏差，并通过记录共同基金持久性的共同因素和以成本为基础的解释。第I节讨论的数据库和其他幸存者偏置校正数据集的关系。第二节介绍车型性能测量被动中年男人基准的股票投资组合，其产生的定价误差估计。第三节文件，并解释了共同基金的回报，一年的持久性和第四节进一步解释结果。第五节探讨和解释长期的持久性，第六节总结。Equity funds 证券投资资金The table reports time-series averages of annual cross-sectional averages from 1962 to 1993. TNA is total net assets, Flow is the percentage change in TNA adjusted for investment return and mutual fund mergers. Exp ratio is total annual management and administrative expenses divided by average TNA. Mturn is modified turnover and represents reported turnover plus 0.5 times Flow. Maximum load is the total of maximum front-end, rear-end, and deferred sales charges as a percentage of the investment. Live funds are those in operation at the end of the sample, December 31, 1993. Dead funds are those that discontinued operations prior to this dat该表报告从1962年至1993年的每年代表性的平均值的时间序列平均值。 TNA是总资产净值，FLOW（流量）是为了投资回报及互惠基金合并调整TNA，其变化的百分比在。Exp ratio是年度总的管理及行政费用除以平均TNA。 Mturn是修改后的营业额，它代表披露的营业额加上0.5倍的FLOW。最大负荷是最大前端，后端，作为投资的百分比的递延销售手续费的总和。实时资金是操作结束时的样本， 1993年12月31日。无效资金是指那些在此日期之前已终止经营业务的资金Tna=总资产净值Flow=在此期间，包括所有已知的股权投资基金。我得到的数据上幸存的资金，并自1989年以来已经消失，从Micropal/Invest- MENT公司数据，公司（ ICDI ）的资金。对于所有其他nonsurviving资金，数据收集杂志FundScope ，美国巴布森报告， Wiesenberger投资公司，华尔街日报，和过去的印刷报告ICDI 。数据库建设一个更详细的说明见卡尔哈特（ 1995年a ）我的样品共包括1,892多元化的股权投资基金和基金16,109年。范例省略了行业基金，国际基金，平衡型基金。其余资金几乎均分侵略性的增长，长期的增长，以及增长和收入类别。平均一年，样本包括509只基金平均总净资产（ TNA ） ， 2.18亿美元和1.14 ，每年的平均费用。此外，基金交易在平均一年其资产价值（ Mturn ）的77.3 。由于披露的营业额是平均TNA的采购和销售最低的，我得到Mturn加入录得营业额的一半在TNA的百分比变化调整投资回报和兼并。此外，在整个样本中， 64.5 的资金负荷费，平均7.33收取。My sample includes a total of 1,892 diversified equity funds and 16,109 fund years. The sample omits sector funds, international funds, and balanced funds. The remaining funds are almost equally divided among aggressive growth, long- term growth, and growth-and-income categories. In an average year, the sample includes 509 funds with average total net assets (TNA) of $218 million and average expenses of 1.14 percent per year. In addition, funds trade 77.3 percent of the value of their assets (Mturn) in an average year. Since reported turnover is the minimum of purchases and sales over average TNA, I obtain Mturn by adding to reported turnover one-half of the percentage change in TNA adjusted for investment returns and mergers. Also, over the full sample, 64.5 percent of funds charge load fees, which average 7.33 percent.By December 31, 1993, about one-third of the total funds in my sample had ceased operations, so a sizeable portion of the database is not observable in most commercially available mutual fund databases. Thus, survivor bias is an important issue in mutual fund research. (See Brown, Goetzmann, Ibbotson, and Ross (1992), Carhart (1995b), and Wermers (1996).) While my sample is, to my knowledge, the largest and most, complete survivor-bias-free mutual fund database currently available, Grinblatt and Titman (1989), Malkiel (1995), Brown and Goetzmann (1995), and Wermers (1996) use similar data- bases to study mutual funds. Grinblatt and Titman (1989) and Wermers (1996) use quarterly snapshots of the mutual funds underlying stock holdings since 1975 to estimate returns gross of transactions costs and expense ratios, whereas my data set uses only the net returns. Malkiel (1995) uses quarterly data from 1971 to 1991, obtained from Lipper Analytical Services. Although Malkiel studies diversified equity funds, his data set includes about 100 fewer funds each year than mine, raising the possibility of some selection bias in the Lipper data set. (We both exclude balanced, sector, and international funds.) Nonetheless, Malkiels mean mutual fund return estimate from 1982 to 1990, 12.9 percent, is very close to the 13 percent that I find. 1993年12月31日前，在我的样本中约三分之一的资金总额已停止运作，所以在大部分市售的共同基金数据库中数据库的一个相当大的部分是无法观测的。因此，幸存者偏见是共同基金研究的一个重要问题。 （参见Brown Goetzmann，Ibbotson, 和Ross (1992), Carhart和Wermers （ 1996） ）虽然据我所知我的样本是目前规模最大，当前可用的最完整的幸存者无偏见的互惠基金数据库，Grinblatt和Titman （1989） ，Malkiel (1995), Brown 和 Goetzmann (1995),和Wermers （1996）使用类似的数据研究共同基金。Grinblatt和Titman （1989）和Wermers （1996）使用共同基金持有的标的股票的季度“快照”，自1975年以来回报率估计交易成本和费用率汇报总额 ，而我的数据集只使用净回报。Malkiel（1995）使用季度数据， 1971年至1991年，从理柏分析服务公司（）获得。虽然Malkiel研究多元化的股权投资基金，他的数据集包括比我每年少约100支基金，提高理柏的数据设置一些选择偏倚的可能性。 （我们俩都排除平衡，部门和国际基金。 ）尽管如此，Malkiel由1982年至1990年共同基金的平均回报估计， 12.9 ，和我的13是非常接近的。Brown and Goetzmann (1995) study a sample of mutual funds very similar to mine, but calculate their returns differently. Their sample is from the Wiesenberger Investment Companies annual volumes from 1976 to 1988. They calculate annual returns from the changes in net asset value per share (NAV), and income and capital gains distributions reported annually in Wiesenberger. As Brown and Goetzmann acknowledge, their data suffer from some selection bias, because the first years of new funds and last years of dead funds are missing. In addition, because funds voluntarily report this information to Wiesenberger, some funds may not report data in years of poor performance. Working in the opposite direction, Brown and Goetzmann calculate return as the sum of the percentage change in NAV (adjusted for capital gains distribu- tions when available) and percentage income return. This procedure biases their return estimates downward somewhat, since it ignores dividend rein-vestment. My data set mitigates these problems because I obtain monthly total returns from multiple sources and so have very few missing returns. In addition, I obtain from ICDI the reinvestment NAVs for capital gains and income distributions. Over the 1976 to 1988 period, Brown and Goetzmann report a mean annual return estimate of 14.5 percent, very close to the 14.3 percent in my data set. By these calculations, selection bias accounts for at least 20 basis points per year in Brown and Goetzmanns sample. It could be somewhat more, however, due to the downward bias in their return calculations. Brown and Goetzmann（1995）研究共同基金的样本和我非常相似，但以不同的方式计算其回报。他们的样品来自Wiesenberger投资公司，1976年至1988年逐年产量。他们从Wiesenberger每年报告的每股资产净值变化（NAV），收入和资本收益分配计算年度回报。正如Brown and Goetzmann承认，他们的数据受到一些选择偏倚影响，因为第一年新资金和过去几年的dead funds缺失。另外，因为基金自愿自觉报告此信息给Wiesenberger，一些基金可能不会报告表现不佳年度的报告数据。在相反的方向工作，Brown and Goetzmann计算回报用：资产净值nva变化的百分比（需要时按资本收益分布调整）和收入回报百分比之和。此过程使投资回报向下估计有点偏离，因为它忽略了股息再投资。我的数据集减轻这些问题，因为我从多个来源获得每月的总回报，所以很少有缺漏。此外，我从ICDI获得的资本收益和收入分配的再投资资产净值的数据。在1976年至1988年期间，Brown and Goetzmann报告平均年度回报率估计为14.5 ，与我的数据集14.3非常接近。通过这些计算，选择偏差解释了在Brown and Goetzmann的样本中每年至少20个偏差基点。但是，由于其收益计算存在向下的偏差，这将可能更多。I employ two models of performance measurement: the Capital Asset Pricing Model (CAPM) described in Sharpe (1964) and Lintner (1965), and my (Car- hart (1995) 4-factor model. This section briefly describes these models, and evaluates their performance estimates on quantitatively-managed portfolios of New York Stock Exchange (NYSE), American Stock Exchange (Amex), and Nasdaq stocks. For comparative purposes, this section also reports perfor- mance estimates from Fama and Frenchs (1993) 3-factor model.我采用两种绩效测量的模型：Sharpe (1964) and Lintner (1965)的资本资产定价模型（CAPM ），和我（CARHART （1995）的四因素模型。本节简要介绍这些模型，并评估他们的表现按纽约证券交易所（ NYSE ） ，美国证券交易所（AMEX） ，纳斯达克股市定量管理的投资组合估计。为便于比较，本节还报告Fama and Frenchs（ 1993年）的三因子模型的表现估计。I construct my 4-factor model using Fama and Frenchs (1993) 3-factor model plus an additional factor capturing Jegadeesh and Titmans (1993) one-year momentum anomaly. The 4-factor model is consistent with a model of market equilibrium with four risk factors. Alternately, it may be interpreted as a performance attribution model, where the coefficients and premia on the factor-mimicking portfolios indicate the proportion of mean return attributable to four elementary strategies: high versus low beta stocks, large versus small market capitalization stocks, value versus growth stocks, and one-year return momentum versus contrarian stocks. I employ the model to explain returns, and leave risk interpretations to the reader.四因素模型与市场均衡模型是一致的，我建立我的四因素模型使用Fama and Frenchs（1993年）的三因子模型加上一个额外的因素捕获Jegadeesh和Titman（1993年）一年的势头异常，四因素模型和四个风险因素市场平衡模型是一致的。另外，它可能会作为业绩归因模型，因子模拟投资组合的系数和溢价，表明平均回报的比例归属四个基本的策略：高与低贝塔的股票，大与小市值个股，价值型与成长型的股票，一年期回报势头投资和相反投资股票。我采用的模型“解释”了回报，并留下风险给读者的诠释。I estimate performance relative to the CAPM, 3-factor, and 4-factor models as我估计有关CAPM ，3 - 因子， 4因子的模型表现为：where rit, is the return on a portfolio in excess of the one-month T-bill return; VWRF is the excess return on the CRSP value-weighted portfolio of all NYSE, Amex, and Nasdaq stocks; RMRF is the excess return on a value-weighted aggregate market proxy; and SMB, HML, and PR1YR are returns on value- weighted, zero-investment, factor-mimicking portfolios for size, book-to-mar- ket equity, and one-year momentum in stock returns.Summary statistics on the factor portfolios reported in Table II indicate that the 4-factor model can explain considerable variation in returns. First, note the relatively high variance of the SMB, HML, and PR1YR zero-investment port- folios and their low correlations with each other and the market proxies. This suggests the 4-factor model can explain sizeable time-series variation. Second, the high mean returns on SMB, HML, and PR1YR suggest that these three factors could account for much cross-sectional variation in the mean return on stock portfolios. Rit ，是超过为期一个月的短期国库券的回报的投资组合的回报; VWRF是所有纽约证交所，美国证交所和纳斯达克股票（NYSE, Amex, and Nasdaq stocks）CRSP价值加权组合的超额收益; RMRF是一个超额收益价值加权的总市值代理和中小企业， HML， PR1YR是加权值，零基投资，规模因子模拟投资组合，帐面价值和市场价值权，股票回报的一年的势头，的回报。总结表二报告的因素组合的统计数据表明，四因子模型可以解释相当大的投资回报变化。首先，请注意相对较高的SMB ， HML， PR1YR的零投资投资组合和它们彼此与市场代理较低的相关性。这表明四因素模型可以解释相当大的时间序列变化。其次，在SMB ， HML， PR1YR上的高平均回报表明这三个因素可以解释很大部分股票投资组合的平均回报的代表性变化。In addition, the low cross-correlations imply that multicol- linearity does not substantially affect the estimated 4-factor model loadings. In tests not reported, I find that the 4-factor model substantially improves on the average pricing errors of the CAPM and the 3-factor model.I estimate pricing errors on 27 quantitatively-managed portfolios of stocks from Carhart, Krail, Stevens, and Welch (1996), where the portfolios are formed on the market value of equity, book-to-market equity and trailing eleven-month re- turn lagged one month. Not surprisingly, the 3-factor model improves on the average pricing errors from the CAPM, since it includes both size and book- to-market equity factors. However, the 3-factor model errors are strongly negative for last years loser stock portfolios and strongly positive for last years winner stock portfolios. In contrast, the 4-factor model noticeably re- duces the average pricing errors relative to both the CAPM and the 3-factor model. For comparative purposes, the mean absolute errors from the CAPM, 3-factor, and 4-factor models are 0.35 percent, 0.31 percent, and 0.14 percent per month, respectively. In addition, the 4-factor model eliminates almost all of the patterns in pricing errors, indicating that it well describes the cross- sectional variation in average stock returns.此外，低交叉相关意味着，基本上不影响的MULTICOL非线性估计的4因子模型负荷。在没有报告的测试中，我发现四因子模型显着提高CAPM和三因子模型中的平均定价错误。我估计27个定量管理的股票投资组合的定价错误，得自Carhart, Krail, Stevens, and Welch（ 1996年） ，在投资组合上形成的股权账面市场股票的市场价值，跟踪十一个月再反过来滞后一个月。